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Fazong Li on Muck Rack

Fazong Li

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Articles

Lightweight detection of companion weeds in rapeseed based on improved YOLOv13n

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By Rufu Hu, Fachao Jiang, Sennan Song, Fazong Li
| Nature Verified
Abstract To solve the problems in accurately and efficiently detecting weeds in rapeseed fields under complex conditions, this paper proposes an improved MMB-YOLO model based on YOLOv13n. This model is built upon YOLOv13n and employs the MobileNetV3 lightweight architecture as the backbone network to decrease the model complexity. A lightweight detection head named MBConv is incorporated to reduce the computational cost while keeping good feature extraction ability.
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